Please use this identifier to cite or link to this item:
|Title:||Online feature selection for model-based reinforcement learning|
|Source:||Nguyen, T.T.,Li, Z.,Silander, T.,Leong, T.-Y. (2013). Online feature selection for model-based reinforcement learning. 30th International Conference on Machine Learning, ICML 2013 (PART 1) : 498-506. ScholarBank@NUS Repository.|
|Abstract:||We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition models through predicting how the actions change the world. We introduce an online sparse coding learning technique for feature selection in high-dimensional spaces. We derive theoretical guarantees for our framework and empirically demonstrate its practicality in both simulated and real robotics domains. Copyright 2013 by the author(s).|
|Source Title:||30th International Conference on Machine Learning, ICML 2013|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 20, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.